Computer implemented systems and methods for optimization of a product inventory by intelligent distribution of inbound products

ABSTRACT

Computer-implemented systems and methods for intelligent generation of purchase orders are disclosed. The systems and methods may be configured to: receive an order quantity for a product from a system determining optimal order quantities for products based on demand forecast data; prioritize the order quantity based on actual constraints; and assign the prioritized order quantity to one or more locations.

TECHNICAL FIELD

The present disclosure generally relates to computerized methods andsystems for optimizing product inventory by intelligently distributingincoming products. In particular, embodiments of the present disclosurerelate to inventive and unconventional systems that assign orderquantities of products to fulfillment centers by prioritizing theproducts based on real-world constraints.

BACKGROUND

Fulfillment centers (FCs) encounter more than millions of products dailyas they operate to fulfill consumer orders as soon as the orders areplaced and enable shipping carriers to pick up shipments. Operations formanaging inventory inside FCs may include receiving merchandise fromsellers, stowing the received merchandise for easy picking access,packing the items, verifying the order, and package delivery. Althoughcurrently existing FCs and systems for inventory management in FCs areconfigured to handle large volumes of incoming and outgoing merchandise,a common issue arises when a FC receives more than can be handled ordersbecause orders are not distributed adequately among multiple FCs. Forexample, a merchant associated with FCs may order large volumes ofproducts from suppliers for a peak season, but the FCs do not havesufficient resources to receive the ordered products in timely manner.This leads to massive backlog problems at the FCs by slowing down everyreceiving process that will eventually accumulate the problems. Thebacklog problems may result a loss in sales because it hampers themerchant from circulating products to make profits.

To mitigate such problems, conventional inventory management systemsinvest in logistics by hiring more workers in FC. The benefit of hiringmore workers is that additional workers will help with backlogs.Depending on the workers' capabilities, it may be possible to deployworkers in several different roles. While these computerized systemsattempt to solve backlog problems in an efficient manner, many times thecost of hiring more workers does not optimize productivity.

Therefore, there is a need for improved methods and systems for keepingproduct inventory at an optimum level by intelligently assigning properquantity of inbound products to a plurality of FCs.

SUMMARY

One aspect of the present disclosure is directed to acomputer-implemented system for intelligent distribution of products.The system may comprise a memory storing instructions and at least oneprocessor configured to execute the instructions. The instructions maycomprise receiving an order quantity for a product from a systemdetermining optimal order quantities for products based on demandforecast data; prioritizing the order quantity based on real-worldconstraints at a national level; and assigning the prioritized orderquantity to one or more locations.

Yet another aspect of the present disclosure is directed to acomputer-implemented method for intelligent distribution of products.The method may comprise receiving an order quantity for a product from asystem determining optimal order quantities for products based on demandforecast data; prioritizing the order quantity based on real-worldconstraints at a national level; and assigning the prioritized orderquantity to one or more locations.

Still further, another aspect of the present disclosure is directed to acomputer-implemented system for intelligent distribution of products.The system may comprise receiving one or more demand forecast quantitiesof one or more products, the products corresponding to one or moreproduct identifiers, and the demand forecast quantities comprising ademand forecast quantity for each product for each unit of time;receiving supplier statistics data for one or more suppliers, thesuppliers being associated with a portion of the products; receivingcurrent product inventory levels and currently ordered quantities of theproducts; determining order quantities for the products based at leaston the demand forecast quantities, the supplier statistics data, and thecurrent product inventory levels; prioritizing the order quantitiesbased on real-world constraints at a national level; assigning theprioritized order quantities to one or more locations; and generatingpurchase orders to the suppliers for the products based on the assignedorder quantities.

Other systems, methods, and computer-readable media are also discussedherein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic block diagram illustrating an exemplaryembodiment of a network comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations, consistent with the disclosed embodiments.

FIG. 1B depicts a sample Search Result Page (SRP) that includes one ormore search results satisfying a search request along with interactiveuser interface elements, consistent with the disclosed embodiments.

FIG. 1C depicts a sample Single Display Page (SDP) that includes aproduct and information about the product along with interactive userinterface elements, consistent with the disclosed embodiments.

FIG. 1D depicts a sample Cart page that includes items in a virtualshopping cart along with interactive user interface elements, consistentwith the disclosed embodiments.

FIG. 1E depicts a sample Order page that includes items from the virtualshopping cart along with information regarding purchase and shipping,along with interactive user interface elements, consistent with thedisclosed embodiments.

FIG. 2 is a diagrammatic illustration of an exemplary fulfillment centerconfigured to utilize disclosed computerized systems, consistent withthe disclosed embodiments.

FIG. 3 is a schematic block diagram illustrating an exemplary embodimentof a networked environment comprising computerized systems for keepingproduct inventory at an optimum level, consistent with the disclosedembodiments.

FIG. 4 is a flowchart of an exemplary computerized process forintelligent distribution of products to keep an inventory at an optimumlevel in FC.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to thecomponents and steps illustrated in the drawings, and the illustrativemethods described herein may be modified by substituting, reordering,removing, or adding steps to the disclosed methods. Accordingly, thefollowing detailed description is not limited to the disclosedembodiments and examples. Instead, the proper scope of the invention isdefined by the appended claims.

Embodiments of the present disclosure are directed tocomputer-implemented systems and methods for optimizing productinventory by assigning proper quantities of products to fulfillmentcenters. The disclosed embodiments provide innovative technical featuresthat allow for automated product assignment based on real-worldconstraints at a national level. For example, the disclosed embodimentsenable efficient assignment of an order quantity for a product tofulfillment centers by utilizing a set of rules or a logisticalregression model on real-world constraints at a national level.

Referring to FIG. 1A, a schematic block diagram 100 illustrating anexemplary embodiment of a system comprising computerized systems forcommunications enabling shipping, transportation, and logisticsoperations is shown. As illustrated in FIG. 1A, system 100 may include avariety of systems, each of which may be connected to one another viaone or more networks. The systems may also be connected to one anothervia a direct connection, for example, using a cable. The depictedsystems include a shipment authority technology (SAT) system 101, anexternal front end system 103, an internal front end system 105, atransportation system 107, mobile devices 107A, 1078, and 107C, sellerportal 109, shipment and order tracking (SOT) system 111, fulfillmentoptimization (FO) system 113, fulfillment messaging gateway (FMG) 115,supply chain management (SCM) system 117, warehouse management system119, mobile devices 119A, 119B, and 119C (depicted as being inside offulfillment center (FC) 200), 3^(rd) party fulfillment systems 121A,121B, and 121C, fulfillment center authorization system (FC Auth) 123,and labor management system (LMS) 125.

SAT system 101, in some embodiments, may be implemented as a computersystem that monitors order status and delivery status. For example, SATsystem 101 may determine whether an order is past its Promised DeliveryDate (PDD) and may take appropriate action, including initiating a neworder, reshipping the items in the non-delivered order, canceling thenon-delivered order, initiating contact with the ordering customer, orthe like. SAT system 101 may also monitor other data, including output(such as a number of packages shipped during a particular time period)and input (such as the number of empty cardboard boxes received for usein shipping). SAT system 101 may also act as a gateway between differentdevices in system 100, enabling communication (e.g., usingstore-and-forward or other techniques) between devices such as externalfront end system 103 and FO system 113.

External front end system 103, in some embodiments, may be implementedas a computer system that enables external users to interact with one ormore systems in system 100. For example, in embodiments where system 100enables the presentation of systems to enable users to place an orderfor an item, external front end system 103 may be implemented as a webserver that receives search requests, presents item pages, and solicitspayment information. For example, external front end system 103 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, external front end system 103may run custom web server software designed to receive and processrequests from external devices (e.g., mobile device 102A or computer102B), acquire information from databases and other data stores based onthose requests, and provide responses to the received requests based onacquired information.

In some embodiments, external front end system 103 may include one ormore of a web caching system, a database, a search system, or a paymentsystem. In one aspect, external front end system 103 may comprise one ormore of these systems, while in another aspect, external front endsystem 103 may comprise interfaces (e.g., server-to-server,database-to-database, or other network connections) connected to one ormore of these systems.

An illustrative set of steps, illustrated by FIGS. 1B, 1C, 1D, and 1E,will help to describe some operations of external front end system 103.External front end system 103 may receive information from systems ordevices in system 100 for presentation and/or display. For example,external front end system 103 may host or provide one or more web pages,including a Search Result Page (SRP) (e.g., FIG. 1B), a Single DetailPage (SDP) (e.g., FIG. 1C), a Cart page (e.g., FIG. 1D), or an Orderpage (e.g., FIG. 1E). A user device (e.g., using mobile device 102A orcomputer 102B) may navigate to external front end system 103 and requesta search by entering information into a search box. External front endsystem 103 may request information from one or more systems in system100. For example, external front end system 103 may request informationfrom FO System 113 that satisfies the search request. External front endsystem 103 may also request and receive (from FO System 113) a PromisedDelivery Date or “PDD” for each product included in the search results.The PDD, in some embodiments, may represent an estimate of when apackage containing the product will arrive at the user's desiredlocation or a date by which the product is promised to be delivered atthe user's desired location if ordered within a particular period oftime, for example, by the end of the day (11:59 PM). (PDD is discussedfurther below with respect to FO System 113.)

External front end system 103 may prepare an SRP (e.g., FIG. 1B) basedon the information. The SRP may include information that satisfies thesearch request. For example, this may include pictures of products thatsatisfy the search request. The SRP may also include respective pricesfor each product, or information relating to enhanced delivery optionsfor each product, PDD, weight, size, offers, discounts, or the like.External front end system 103 may send the SRP to the requesting userdevice (e.g., via a network).

A user device may then select a product from the SRP, e.g., by clickingor tapping a user interface, or using another input device, to select aproduct represented on the SRP. The user device may formulate a requestfor information on the selected product and send it to external frontend system 103. In response, external front end system 103 may requestinformation related to the selected product. For example, theinformation may include additional information beyond that presented fora product on the respective SRP. This could include, for example, shelflife, country of origin, weight, size, number of items in package,handling instructions, or other information about the product. Theinformation could also include recommendations for similar products(based on, for example, big data and/or machine learning analysis ofcustomers who bought this product and at least one other product),answers to frequently asked questions, reviews from customers,manufacturer information, pictures, or the like.

External front end system 103 may prepare an SDP (Single Detail Page)(e.g., FIG. 1C) based on the received product information. The SDP mayalso include other interactive elements such as a “Buy Now” button, a“Add to Cart” button, a quantity field, a picture of the item, or thelike. The SDP may further include a list of sellers that offer theproduct. The list may be ordered based on the price each seller offerssuch that the seller that offers to sell the product at the lowest pricemay be listed at the top. The list may also be ordered based on theseller ranking such that the highest ranked seller may be listed at thetop. The seller ranking may be formulated based on multiple factors,including, for example, the seller's past track record of meeting apromised PDD. External front end system 103 may deliver the SDP to therequesting user device (e.g., via a network).

The requesting user device may receive the SDP which lists the productinformation. Upon receiving the SDP, the user device may then interactwith the SDP. For example, a user of the requesting user device mayclick or otherwise interact with a “Place in Cart” button on the SDP.This adds the product to a shopping cart associated with the user. Theuser device may transmit this request to add the product to the shoppingcart to external front end system 103.

External front end system 103 may generate a Cart page (e.g., FIG. 1D).The Cart page, in some embodiments, lists the products that the user hasadded to a virtual “shopping cart.” A user device may request the Cartpage by clicking on or otherwise interacting with an icon on the SRP,SDP, or other pages. The Cart page may, in some embodiments, list allproducts that the user has added to the shopping cart, as well asinformation about the products in the cart such as a quantity of eachproduct, a price for each product per item, a price for each productbased on an associated quantity, information regarding PDD, a deliverymethod, a shipping cost, user interface elements for modifying theproducts in the shopping cart (e.g., deletion or modification of aquantity), options for ordering other product or setting up periodicdelivery of products, options for setting up interest payments, userinterface elements for proceeding to purchase, or the like. A user at auser device may click on or otherwise interact with a user interfaceelement (e.g., a button that reads “Buy Now”) to initiate the purchaseof the product in the shopping cart. Upon doing so, the user device maytransmit this request to initiate the purchase to external front endsystem 103.

External front end system 103 may generate an Order page (e.g., FIG. 1E)in response to receiving the request to initiate a purchase. The Orderpage, in some embodiments, re-lists the items from the shopping cart andrequests input of payment and shipping information. For example, theOrder page may include a section requesting information about thepurchaser of the items in the shopping cart (e.g., name, address, e-mailaddress, phone number), information about the recipient (e.g., name,address, phone number, delivery information), shipping information(e.g., speed/method of delivery and/or pickup), payment information(e.g., credit card, bank transfer, check, stored credit), user interfaceelements to request a cash receipt (e.g., for tax purposes), or thelike. External front end system 103 may send the Order page to the userdevice.

The user device may enter information on the Order page and click orotherwise interact with a user interface element that sends theinformation to external front end system 103. From there, external frontend system 103 may send the information to different systems in system100 to enable the creation and processing of a new order with theproducts in the shopping cart.

In some embodiments, external front end system 103 may be furtherconfigured to enable sellers to transmit and receive informationrelating to orders.

Internal front end system 105, in some embodiments, may be implementedas a computer system that enables internal users (e.g., employees of anorganization that owns, operates, or leases system 100) to interact withone or more systems in system 100. For example, in embodiments wherenetwork 101 enables the presentation of systems to enable users to placean order for an item, internal front end system 105 may be implementedas a web server that enables internal users to view diagnostic andstatistical information about orders, modify item information, or reviewstatistics relating to orders. For example, internal front end system105 may be implemented as a computer or computers running software suchas the Apache HTTP Server, Microsoft Internet Information Services(IIS), NGINX, or the like. In other embodiments, internal front endsystem 105 may run custom web server software designed to receive andprocess requests from systems or devices depicted in system 100 (as wellas other devices not depicted), acquire information from databases andother data stores based on those requests, and provide responses to thereceived requests based on acquired information.

In some embodiments, internal front end system 105 may include one ormore of a web caching system, a database, a search system, a paymentsystem, an analytics system, an order monitoring system, or the like. Inone aspect, internal front end system 105 may comprise one or more ofthese systems, while in another aspect, internal front end system 105may comprise interfaces (e.g., server-to-server, database-to-database,or other network connections) connected to one or more of these systems.

Transportation system 107, in some embodiments, may be implemented as acomputer system that enables communication between systems or devices insystem 100 and mobile devices 107A-107C. Transportation system 107, insome embodiments, may receive information from one or more mobiledevices 107A-107C (e.g., mobile phones, smart phones, PDAs, or thelike). For example, in some embodiments, mobile devices 107A-107C maycomprise devices operated by delivery workers. The delivery workers, whomay be permanent, temporary, or shift employees, may utilize mobiledevices 107A-107C to effect delivery of packages containing the productsordered by users. For example, to deliver a package, the delivery workermay receive a notification on a mobile device indicating which packageto deliver and where to deliver it. Upon arriving at the deliverylocation, the delivery worker may locate the package (e.g., in the backof a truck or in a crate of packages), scan or otherwise capture dataassociated with an identifier on the package (e.g., a barcode, an image,a text string, an RFID tag, or the like) using the mobile device, anddeliver the package (e.g., by leaving it at a front door, leaving itwith a security guard, handing it to the recipient, or the like). Insome embodiments, the delivery worker may capture photo(s) of thepackage and/or may obtain a signature using the mobile device. Themobile device may send information to transportation system 107including information about the delivery, including, for example, time,date, GPS location, photo(s), an identifier associated with the deliveryworker, an identifier associated with the mobile device, or the like.Transportation system 107 may store this information in a database (notpictured) for access by other systems in system 100. Transportationsystem 107 may, in some embodiments, use this information to prepare andsend tracking data to other systems indicating the location of aparticular package.

In some embodiments, certain users may use one kind of mobile device(e.g., permanent workers may use a specialized PDA with custom hardwaresuch as a barcode scanner, stylus, and other devices) while other usersmay use other kinds of mobile devices (e.g., temporary or shift workersmay utilize off-the-shelf mobile phones and/or smartphones).

In some embodiments, transportation system 107 may associate a user witheach device. For example, transportation system 107 may store anassociation between a user (represented by, e.g., a user identifier, anemployee identifier, or a phone number) and a mobile device (representedby, e.g., an International Mobile Equipment Identity (IMEI), anInternational Mobile Subscription Identifier (IMSI), a phone number, aUniversal Unique Identifier (UUID), or a Globally Unique Identifier(GUID)). Transportation system 107 may use this association inconjunction with data received on deliveries to analyze data stored inthe database in order to determine, among other things, a location ofthe worker, an efficiency of the worker, or a speed of the worker.

Seller portal 109, in some embodiments, may be implemented as a computersystem that enables sellers or other external entities to electronicallycommunicate with one or more systems in system 100. For example, aseller may utilize a computer system (not pictured) to upload or provideproduct information, order information, contact information, or thelike, for products that the seller wishes to sell through system 100using seller portal 109.

Shipment and order tracking system 111, in some embodiments, may beimplemented as a computer system that receives, stores, and forwardsinformation regarding the location of packages containing productsordered by customers (e.g., by a user using devices 102A-102B). In someembodiments, shipment and order tracking system 111 may request or storeinformation from web servers (not pictured) operated by shippingcompanies that deliver packages containing products ordered bycustomers.

In some embodiments, shipment and order tracking system 111 may requestand store information from systems depicted in system 100. For example,shipment and order tracking system 111 may request information fromtransportation system 107. As discussed above, transportation system 107may receive information from one or more mobile devices 107A-107C (e.g.,mobile phones, smart phones, PDAs, or the like) that are associated withone or more of a user (e.g., a delivery worker) or a vehicle (e.g., adelivery truck). In some embodiments, shipment and order tracking system111 may also request information from warehouse management system (WMS)119 to determine the location of individual products inside of afulfillment center (e.g., fulfillment center 200). Shipment and ordertracking system 111 may request data from one or more of transportationsystem 107 or WMS 119, process it, and present it to a device (e.g.,user devices 102A and 1028) upon request.

Fulfillment optimization (FO) system 113, in some embodiments, may beimplemented as a computer system that stores information for customerorders from other systems (e.g., external front end system 103 and/orshipment and order tracking system 111). FO system 113 may also storeinformation describing where particular items are held or stored. Forexample, certain items may be stored only in one fulfillment center,while certain other items may be stored in multiple fulfillment centers.In still other embodiments, certain fulfilment centers may be designedto store only a particular set of items (e.g., fresh produce or frozenproducts). FO system 113 stores this information as well as associatedinformation (e.g., quantity, size, date of receipt, expiration date,etc.).

FO system 113 may also calculate a corresponding PDD (promised deliverydate) for each product. The PDD, in some embodiments, may be based onone or more factors. For example, FO system 113 may calculate a PDD fora product based on a past demand for a product (e.g., how many timesthat product was ordered during a period of time), an expected demandfor a product (e.g., how many customers are forecast to order theproduct during an upcoming period of time), a network-wide past demandindicating how many products were ordered during a period of time, anetwork-wide expected demand indicating how many products are expectedto be ordered during an upcoming period of time, one or more counts ofthe product stored in each fulfillment center 200, which fulfillmentcenter stores each product, expected or current orders for that product,or the like.

In some embodiments, FO system 113 may determine a PDD for each producton a periodic basis (e.g., hourly) and store it in a database forretrieval or sending to other systems (e.g., external front end system103, SAT system 101, shipment and order tracking system 111). In otherembodiments, FO system 113 may receive electronic requests from one ormore systems (e.g., external front end system 103, SAT system 101,shipment and order tracking system 111) and calculate the PDD on demand.

Fulfilment messaging gateway (FMG) 115, in some embodiments, may beimplemented as a computer system that receives a request or response inone format or protocol from one or more systems in system 100, such asFO system 113, converts it to another format or protocol, and forward itin the converted format or protocol to other systems, such as WMS 119 or3^(rd) party fulfillment systems 121A, 121B, or 121C, and vice versa.

Supply chain management (SCM) system 117, in some embodiments, may beimplemented as a computer system that performs forecasting functions.For example, SCM system 117 may forecast a level of demand for aparticular product based on, for example, based on a past demand forproducts, an expected demand for a product, a network-wide past demand,a network-wide expected demand, a count products stored in eachfulfillment center 200, expected or current orders for each product, orthe like. In response to this forecasted level and the amount of eachproduct across all fulfillment centers, SCM system 117 may generate oneor more purchase orders to purchase and stock a sufficient quantity tosatisfy the forecasted demand for a particular product.

Warehouse management system (WMS) 119, in some embodiments, may beimplemented as a computer system that monitors workflow. For example,WMS 119 may receive event data from individual devices (e.g., devices107A-107C or 119A-119C) indicating discrete events. For example, WMS 119may receive event data indicating the use of one of these devices toscan a package. As discussed below with respect to fulfillment center200 and FIG. 2, during the fulfillment process, a package identifier(e.g., a barcode or RFID tag data) may be scanned or read by machines atparticular stages (e.g., automated or handheld barcode scanners, RFIDreaders, high-speed cameras, devices such as tablet 119A, mobiledevice/PDA 1198, computer 119C, or the like). WMS 119 may store eachevent indicating a scan or a read of a package identifier in acorresponding database (not pictured) along with the package identifier,a time, date, location, user identifier, or other information, and mayprovide this information to other systems (e.g., shipment and ordertracking system 111).

WMS 119, in some embodiments, may store information associating one ormore devices (e.g., devices 107A-107C or 119A-119C) with one or moreusers associated with system 100. For example, in some situations, auser (such as a part- or full-time employee) may be associated with amobile device in that the user owns the mobile device (e.g., the mobiledevice is a smartphone). In other situations, a user may be associatedwith a mobile device in that the user is temporarily in custody of themobile device (e.g., the user checked the mobile device out at the startof the day, will use it during the day, and will return it at the end ofthe day).

WMS 119, in some embodiments, may maintain a work log for each userassociated with system 100. For example, WMS 119 may store informationassociated with each employee, including any assigned processes (e.g.,unloading trucks, picking items from a pick zone, rebin wall work,packing items), a user identifier, a location (e.g., a floor or zone ina fulfillment center 200), a number of units moved through the system bythe employee (e.g., number of items picked, number of items packed), anidentifier associated with a device (e.g., devices 119A-119C), or thelike. In some embodiments, WMS 119 may receive check-in and check-outinformation from a timekeeping system, such as a timekeeping systemoperated on a device 119A-119C.

3^(rd) party fulfillment (3PL) systems 121A-121C, in some embodiments,represent computer systems associated with third-party providers oflogistics and products. For example, while some products are stored infulfillment center 200 (as discussed below with respect to FIG. 2),other products may be stored off-site, may be produced on demand, or maybe otherwise unavailable for storage in fulfillment center 200. 3PLsystems 121A-121C may be configured to receive orders from FO system 113(e.g., through FMG 115) and may provide products and/or services (e.g.,delivery or installation) to customers directly. In some embodiments,one or more of 3PL systems 121A-121C may be part of system 100, while inother embodiments, one or more of 3PL systems 121A-121C may be outsideof system 100 (e.g., owned or operated by a third-party provider).

Fulfillment Center Auth system (FC Auth) 123, in some embodiments, maybe implemented as a computer system with a variety of functions. Forexample, in some embodiments, FC Auth 123 may act as a single-sign on(SSO) service for one or more other systems in system 100. For example,FC Auth 123 may enable a user to log in via internal front end system105, determine that the user has similar privileges to access resourcesat shipment and order tracking system 111, and enable the user to accessthose privileges without requiring a second log in process. FC Auth 123,in other embodiments, may enable users (e.g., employees) to associatethemselves with a particular task. For example, some employees may nothave an electronic device (such as devices 119A-119C) and may insteadmove from task to task, and zone to zone, within a fulfillment center200, during the course of a day. FC Auth 123 may be configured to enablethose employees to indicate what task they are performing and what zonethey are in at different times of day.

Labor management system (LMS) 125, in some embodiments, may beimplemented as a computer system that stores attendance and overtimeinformation for employees (including full-time and part-time employees).For example, LMS 125 may receive information from FC Auth 123, WMA 119,devices 119A-119C, transportation system 107, and/or devices 107A-107C.

The particular configuration depicted in FIG. 1A is an example only. Forexample, while FIG. 1A depicts FC Auth system 123 connected to FO system113, not all embodiments require this particular configuration. Indeed,in some embodiments, the systems in system 100 may be connected to oneanother through one or more public or private networks, including theInternet, an Intranet, a WAN (Wide-Area Network), a MAN(Metropolitan-Area Network), a wireless network compliant with the IEEE802.11a/b/g/n Standards, a leased line, or the like. In someembodiments, one or more of the systems in system 100 may be implementedas one or more virtual servers implemented at a data center, serverfarm, or the like.

FIG. 2 depicts a fulfillment center 200. Fulfillment center 200 is anexample of a physical location that stores items for shipping tocustomers when ordered. Fulfillment center (FC) 200 may be divided intomultiple zones, each of which are depicted in FIG. 2. These “zones,” insome embodiments, may be thought of as virtual divisions betweendifferent stages of a process of receiving items, storing the items,retrieving the items, and shipping the items. So while the “zones” aredepicted in FIG. 2, other divisions of zones are possible, and the zonesin FIG. 2 may be omitted, duplicated, or modified in some embodiments.

Inbound zone 203 represents an area of FC 200 where items are receivedfrom sellers who wish to sell products using system 100 from FIG. 1A.For example, a seller may deliver items 202A and 202B using truck 201.Item 202A may represent a single item large enough to occupy its ownshipping pallet, while item 202B may represent a set of items that arestacked together on the same pallet to save space.

A worker will receive the items in inbound zone 203 and may optionallycheck the items for damage and correctness using a computer system (notpictured). For example, the worker may use a computer system to comparethe quantity of items 202A and 202B to an ordered quantity of items. Ifthe quantity does not match, that worker may refuse one or more of items202A or 202B. If the quantity does match, the worker may move thoseitems (using, e.g., a dolly, a handtruck, a forklift, or manually) tobuffer zone 205. Buffer zone 205 may be a temporary storage area foritems that are not currently needed in the picking zone, for example,because there is a high enough quantity of that item in the picking zoneto satisfy forecasted demand. In some embodiments, forklifts 206 operateto move items around buffer zone 205 and between inbound zone 203 anddrop zone 207. If there is a need for items 202A or 202B in the pickingzone (e.g., because of forecasted demand), a forklift may move items202A or 202B to drop zone 207.

Drop zone 207 may be an area of FC 200 that stores items before they aremoved to picking zone 209. A worker assigned to the picking task (a“picker”) may approach items 202A and 202B in the picking zone, scan abarcode for the picking zone, and scan barcodes associated with items202A and 202B using a mobile device (e.g., device 119B). The picker maythen take the item to picking zone 209 (e.g., by placing it on a cart orcarrying it).

Picking zone 209 may be an area of FC 200 where items 208 are stored onstorage units 210. In some embodiments, storage units 210 may compriseone or more of physical shelving, bookshelves, boxes, totes,refrigerators, freezers, cold stores, or the like. In some embodiments,picking zone 209 may be organized into multiple floors. In someembodiments, workers or machines may move items into picking zone 209 inmultiple ways, including, for example, a forklift, an elevator, aconveyor belt, a cart, a handtruck, a dolly, an automated robot ordevice, or manually. For example, a picker may place items 202A and 202Bon a handtruck or cart in drop zone 207 and walk items 202A and 202B topicking zone 209.

A picker may receive an instruction to place (or “stow”) the items inparticular spots in picking zone 209, such as a particular space on astorage unit 210. For example, a picker may scan item 202A using amobile device (e.g., device 119B). The device may indicate where thepicker should stow item 202A, for example, using a system that indicatean aisle, shelf, and location. The device may then prompt the picker toscan a barcode at that location before stowing item 202A in thatlocation. The device may send (e.g., via a wireless network) data to acomputer system such as WMS 119 in FIG. 1A indicating that item 202A hasbeen stowed at the location by the user using device 1198.

Once a user places an order, a picker may receive an instruction ondevice 1198 to retrieve one or more items 208 from storage unit 210. Thepicker may retrieve item 208, scan a barcode on item 208, and place iton transport mechanism 214. While transport mechanism 214 is representedas a slide, in some embodiments, transport mechanism may be implementedas one or more of a conveyor belt, an elevator, a cart, a forklift, ahandtruck, a dolly, a cart, or the like. Item 208 may then arrive atpacking zone 211.

Packing zone 211 may be an area of FC 200 where items are received frompicking zone 209 and packed into boxes or bags for eventual shipping tocustomers. In packing zone 211, a worker assigned to receiving items (a“rebin worker”) will receive item 208 from picking zone 209 anddetermine what order it corresponds to. For example, the rebin workermay use a device, such as computer 119C, to scan a barcode on item 208.Computer 119C may indicate visually which order item 208 is associatedwith. This may include, for example, a space or “cell” on a wall 216that corresponds to an order. Once the order is complete (e.g., becausethe cell contains all items for the order), the rebin worker mayindicate to a packing worker (or “packer”) that the order is complete.The packer may retrieve the items from the cell and place them in a boxor bag for shipping. The packer may then send the box or bag to a hubzone 213, e.g., via forklift, cart, dolly, handtruck, conveyor belt,manually, or otherwise.

Hub zone 213 may be an area of FC 200 that receives all boxes or bags(“packages”) from packing zone 211. Workers and/or machines in hub zone213 may retrieve package 218 and determine which portion of a deliveryarea each package is intended to go to, and route the package to anappropriate camp zone 215. For example, if the delivery area has twosmaller sub-areas, packages will go to one of two camp zones 215. Insome embodiments, a worker or machine may scan a package (e.g., usingone of devices 119A-119C) to determine its eventual destination. Routingthe package to camp zone 215 may comprise, for example, determining aportion of a geographical area that the package is destined for (e.g.,based on a postal code) and determining a camp zone 215 associated withthe portion of the geographical area.

Camp zone 215, in some embodiments, may comprise one or more buildings,one or more physical spaces, or one or more areas, where packages arereceived from hub zone 213 for sorting into routes and/or sub-routes. Insome embodiments, camp zone 215 is physically separate from FC 200 whilein other embodiments camp zone 215 may form a part of FC 200.

Workers and/or machines in camp zone 215 may determine which routeand/or sub-route a package 220 should be associated with, for example,based on a comparison of the destination to an existing route and/orsub-route, a calculation of workload for each route and/or sub-route,the time of day, a shipping method, the cost to ship the package 220, aPDD associated with the items in package 220, or the like. In someembodiments, a worker or machine may scan a package (e.g., using one ofdevices 119A-119C) to determine its eventual destination. Once package220 is assigned to a particular route and/or sub-route, a worker and/ormachine may move package 220 to be shipped. In exemplary FIG. 2, campzone 215 includes a truck 222, a car 226, and delivery workers 224A and224B. In some embodiments, truck 222 may be driven by delivery worker224A, where delivery worker 224A is a full-time employee that deliverspackages for FC 200 and truck 222 is owned, leased, or operated by thesame company that owns, leases, or operates FC 200. In some embodiments,car 226 may be driven by delivery worker 224B, where delivery worker224B is a “flex” or occasional worker that is delivering on an as-neededbasis (e.g., seasonally). Car 226 may be owned, leased, or operated bydelivery worker 224B.

FIG. 3 is a schematic block diagram illustrating an exemplary embodimentof a networked environment 300 comprising computerized systems forkeeping product inventory at an optimum level. Environment 300 mayinclude a variety of systems, each of which may be connected to oneanother via one or more networks. The systems may also be connected toone another via a direct connection, for example, using a cable. Thedepicted systems include an FO system 311, an FC database 312, anexternal front end system 313, a supply chain management system 320, andone or more user terminals 330. FO system 311 and external front endsystem 313 may be similar in design, function, or operation to FO system113 and external front end system 103 described above with respect toFIG. 1A.

FC database 312 may be implemented as one or more computer systems thatcollect, accrue, and/or generate various data accrued from variousactivities at FC 200 as described above with respect to FIG. 2. Forexample, data accrued at FC database 312 may include, among others,product identifiers (e.g., stock keeping unit (SKU)) of every producthandled by a particular FC (e.g., FC 200), an inventory level of eachproduct over time, and frequency and occurrences of out of stock eventsfor each product.

In some embodiments, FC database 312 may comprise FC A database 312A, FCB database 312B, and FC C database 312C, which represent databasesassociated with FCs A-C. While only three FCs and corresponding FCdatabases 312A-C are depicted in FIG. 3, the number is only exemplaryand there may be more FCs and a corresponding number of FC databases. Inother embodiments, FC database 312 may be a centralized databasecollecting and storing data from all FCs. Regardless of whether FCdatabase 312 includes individual databases (e.g., 312A-C) or onecentralized database, the databases may include cloud-based databases oron-premise databases. Also in some embodiments, such databases maycomprise one or more hard disk drives, one or more solid state drives,or one or more non-transitory memories.

Supply Chain Management System (SCM) 320 may be similar in design,function, or operation to SCM 117 described above with respect to FIG.1A. Alternatively or additionally, SCM 320 may be configured toaggregate data from FO system 311, FC database 312, and external frontend system 313 in order to forecast a level of demand for a particularproduct and generate one or more purchase orders in a process consistentwith the disclosed embodiments.

In some embodiments, SCM 320 comprises a data science module 321, ademand forecast generator 322, a target inventory plan system (TIP) 323,an inbound prioritization and shuffling system (IPS) 324, a manual ordersubmission platform 325, a purchase order (PO) generator 326, and areport generator 327.

In some embodiments, SCM 320 may comprise one or more processors, one ormore memories, and one or more input/output (I/O) devices. SCM 320 maytake the form of a server, general-purpose computer, a mainframecomputer, a special-purpose computing device such as a graphicalprocessing unit (GPU), laptop, or any combination of these computingdevices. In these embodiments, components of SCM 320 (i.e., data sciencemodule 321, demand forecast generator 322, TIP 323, IPS 324, manualorder submission platform 325, PO generator 326, and report generator327) may be implemented as one or more functional units performed by oneor more processors based on instructions stored in the one or morememories. SCM 320 may be a standalone system, or it may be part of asubsystem, which may be part of a larger system.

Alternatively, components of SCM 320 may be implemented as one or morecomputer systems communicating with each other via a network. In thisembodiment, each of the one or more computer systems may comprise one ormore processors, one or more memories (i.e., non-transitorycomputer-readable media), and one or more input/output (I/O) devices. Insome embodiments, each of the one or more computer systems may take theform of a server, general-purpose computer, a mainframe computer, aspecial-purpose computing device such as a GPU, laptop, or anycombination of these computing devices.

Data science module 321, in some embodiments, may include one or morecomputing devices configured to determine various parameters or modelsfor use by other components of SCM 320. For example, data science module321 may develop a forecast model used by demand forecast generator 322that determines a level of demand for each product. In some embodiments,data science module 321 may retrieve order information from FO system311 and glance view (i.e., number of webpage views for the product) fromexternal front end system 313 to train the forecast model and anticipatea level of future demand. The order information may include salesstatistics such as a number of items sold over time, a number of itemssold during promotion periods, and a number of items sold during regularperiods. Data science module 321 may train the forecast model based onparameters such as the sales statistics, glance view, season, day of theweek, upcoming holidays, and the like. In some embodiments, data sciencemodule 321 may also receive data from inbound zone 203 of FIG. 2 asproducts ordered via POs generated by PO generator 326 are received.Data science module 321 may use such data to determine various supplierstatistics such as a particular supplier's fulfillment ratio (i.e., apercentage of products that are received in a saleable conditioncompared to an ordered quantity), an estimated lead time and shippingperiod, or the like.

Demand forecast generator 322, in some embodiments, may include one ormore computing devices configured to forecast a level of demand for aparticular product using the forecast model developed by data sciencemodule 321. More specifically, the forecast model may output a demandforecast quantity for each product, where the demand forecast quantityis a specific quantity of the product expected to be sold to one or morecustomers in a given period (e.g., a day). In some embodiments, demandforecast generator 322 may output demand forecast quantities for eachgiven period over a predetermined period (e.g., a demand forecastquantity for each day over a 5-week period). Each demand forecastquantity may also comprise a standard deviation quantity (e.g., ±5) or arange (e.g., maximum of 30 and minimum of 25) to provide moreflexibility in optimizing product inventory levels.

TIP 323, in some embodiments, may include one or more computing devicesconfigured to determine a recommended order quantity for each product.TIP 323 may determine the recommended order quantity by firstdetermining preliminary order quantities for the products andconstraining the preliminary order quantities with real-worldconstraints.

TIP 323 may receive a demand forecast quantity for each product fromdemand forecast generator 322. In some embodiments, the demand forecastquantities may be in the form of a table of numerical values organizedby SKU in one dimension and number of units forecasted to be sold for agiven day in the other dimension. The table may also comprise additionaldimensions devoted to other parameters of the demand forecast quantitysuch as standard deviation, maximum, minimum, average, or the like.Alternatively, the demand forecast quantities may take the form ofmultiple arrays of values organized by SKU and dedicated to eachparameter. Other suitable forms of organizing the same data are equallyapplicable as known in the art and are within the scope of thisinvention.

In some embodiments, TIP 323 may receive, from data science module 321,supplier statistics data of one or more suppliers that supply theproducts. The supplier statistics data may comprise a set of information(e.g., fulfillment ratio described above) associated with each supplier.In some embodiments, there may be multiple sets of supplier statisticsdata for a particular supplier where each set of data is associated witha particular product supplied by the supplier.

TIP 323 may also receive, in some embodiments, from FC databases 312,current product inventory levels and currently ordered quantities ofeach product. The current product inventory level may refer to aninstantaneous count of a particular product at the time of dataretrieval, and the currently ordered quantity may refer to a totalquantity of a particular product that has been ordered through one ormore POs generated in the past and is waiting for delivery tocorresponding FCs.

TIP 323 may determine recommended order quantities for each product bydetermining preliminary order quantities for each product and reducingthe preliminary order quantities based on a range of parameters. In someembodiments, a preliminary order quantity for a particular product maybe a function of at least one of its demand forecast quantity, acoverage period, a safety stock period, current inventory level,currently ordered quantity, a critical ratio, and a case quantity. Forexample, TIP 323 may determine a preliminary order quantity with formula(1):

$\begin{matrix}{Q_{p} = {{ceiling}\left( {\frac{\left( {\sum\limits_{n = 0}^{P_{c} + P_{s} - 1}Q_{fn}} \right) - Q_{c} - Q_{o}}{c} \cdot C} \right)}} & (1)\end{matrix}$

where Q_(p) is a preliminary order quantity for a particular product;Q_(fn) is a demand forecast quantity of the product for nth day from thetime of calculation; Q_(c) is the current inventory level of theproduct; Q_(o) is the currently ordered quantity; P_(c) is the coverageperiod; P_(s) is the safety stock period; and C is the case quantity.

As used herein, a coverage period may refer to a length of time (e.g.,number of days) one PO is planned to cover; and a safety stock periodmay refer to an additional length of time (e.g., additional number ofdays) the PO is should cover in case of an unexpected event such as asudden increase in demand or a delayed delivery. For example, given thefollowing table of sample demand forecast quantities for product X, acoverage period for a PO generated at D-day may be 5 and a safety stockperiod may be 1, in which case,

$\sum\limits_{n = 0}^{P_{c} + P_{s} - 1}Q_{fn}$would equal 37+37+35+40+41+34=224.

TABLE 1 Sample demand forecast quantity for product X over 9 daysForecast D D + 1 D + 2 D + 3 D + 4 D + 5 D + 6 D + 7 D + 8 Q_(f) 37 3735 40 41 34 37 39 41

From this quantity, 224 units of product X, TIP 323 may subtract thecurrent inventory level (e.g., 60 units) and the currently orderedquantity (e.g., 40), which comes out to be 124 units. This number maythen be rounded up to a multiple of the case quantity (i.e., the numberof units that the product comes packaged in such as the number of unitsin a box or a pallet) by being divided by the case quantity, beingrounded up to an integer, and being multiplied by the case quantityagain, which, in this example, comes out to be 130 units assuming a casequantity of 10 as an example.

In some embodiments, the coverage period may be a predetermined lengthof time equal to or greater than an expected length of time acorresponding supplier may take to deliver the products from the date ofPO generation. Additionally or alternatively, TIP 323 may also adjustthe coverage period based on other factors such as the day of the week,anticipated delay, or the like. Furthermore, the safety stock period maybe another predetermined length of time designed to increase thepreliminary order quantity as a safety measure. The safety stock periodmay reduce the risk of running out of stock in case of unexpected eventssuch as a sudden increase in demand or an unanticipated shipping delay.In some embodiments, TIP 323 may set the safety stock period based onthe coverage period, where, for example, a safety stock period of 0 dayis added when a coverage period is 1-3 days, 1 day is added when acoverage period is 4-6 days, and 3 days are added when a coverage periodis greater than 7 days.

Despite the complex process of determining the preliminary orderquantities described above, the preliminary order quantity may be basedprimarily on customer demand and not take real-world constraints intoaccount. Steps for accounting for such constraints are thus desired inorder to optimize product inventories. TIP 323, in some embodiments, mayadjust the preliminary order quantities using a set of rules configuredto fine tune the preliminary order quantities based on data such assales statistics, the current product inventory levels and the currentlyordered quantities.

The resulting quantities, recommended order quantities, may betransmitted to PO generator 326. In other embodiments, the resultingquantities may be further processed by IPS 324 to prioritize particularproducts and/or distribute the quantities to one or more FCs asdescribed below with respect to FIG. 4.

In addition, IPS 324, in some embodiments, may include one or morecomputing devices configured to determine a popularity for each product,prioritize the order quantity based on the determined popularity, anddistribute the prioritized order quantity to one or more FCs 200. Theprocesses for determining the popularity, prioritizing, and distributingproducts are described below in more detail with respect to FIG. 4.

Manual order submission platform 325, in some embodiments, may includeone or more computing devices configured to receive user inputs for oneor more manual orders. Manual order submission platform 325 may comprisea user interface accessible by a user via one or more computing devicessuch as internal front end system 105 of FIG. 1A. In one aspect, themanual orders may include extra quantities of certain products that theuser may deem necessary and allow manual adjustments (e.g., increasingor decreasing by a certain amount) of the preliminary order quantities,the recommended order quantities, the prioritized order quantities, orthe distributed order quantities. In another aspect, the manual ordersmay include a total quantity of certain products that should be orderedas determined by an internal user instead of the order quantitiesdetermined by SCM 320. An exemplary process of reconciling theseuser-determined order quantities with SCM-generated order quantities isexplained below in more detail with respect to FIG. 5. Still further, auser may specify, in some embodiments, a particular FC as a receivinglocation so that the manual orders may get assigned to the particularFC. In some embodiments, portions of the order quantities submitted viamanual order submission platform 325 may be marked or flagged (e.g., byupdating a parameter associated with the portion of the order quantity)so that they may not be adjusted (i.e., constrained) by TIP 323 or IPS324.

In some embodiments, manual order submission platform 325 may beimplemented as a computer or computers running software such as theApache HTTP Server, Microsoft Internet Information Services (IIS),NGINX, or the like. In other embodiments, manual order submissionplatform 325 may run a custom web server software designed to receiveand process user inputs from one or more user terminals 330 and provideresponses to the received user inputs.

PO generator 326, in some embodiments, may include one or more computingdevices configured to generate POs to one or more suppliers based on therecommended order quantities or results of the distribution by IPS 324.SCM 320, by this point, would have determined a recommended orderquantity for each product that requires additional inventory and foreach FC 200, where each product has one or more suppliers that procureor manufacture the particular product and ship it to one or more FCs. Aparticular supplier may supply one or more products, and a particularproduct may be supplied by one or more suppliers. When generating POs,PO generator 326 may issue a paper PO to be mailed or faxed to thesupplier or an electronic PO to be transmitted to the same.

Report generator 327, in some embodiments, may include one or morecomputing devices configured to generate reports periodically inresponse to a predetermined protocol or on-demand in response to userinputs via, for example, user terminals 330 or internal front end system105 of FIG. 1A. The reports may range from simple ones that outputcertain information such as the recommended order quantity for aparticular product to complex ones that require analysis of historicaldata and visualize such information in a graph. More specifically,report generator 327 may generate reports including information such ashow order quantities changed from the forecasted quantities to finalquantities at each step of the adjustments performed by SCM 320; ahistory of how much resources at each FC 200 were utilized; differencesbetween the forecasted quantities and the final quantities (i.e.,quantities that had to be reduced from the forecasted quantities inorder to account for real-world limitations) by product category; andthe like.

User terminals 330, in some embodiments, may include one or morecomputing devices configured to enable internal users such as thoseworking at an FC 00 to access SCM 320 via manual order submissionplatform 325 or report generator 327. User terminals 330 may include anycombination of computing devices such as personal computers, mobilephones, smartphones, PDAs, or the like. In some embodiments, theinternal users may use user terminals 330 to access a web interfaceprovided by manual order submission platform 325 in order to submit oneor more manual orders.

FIG. 4 is a flowchart of an exemplary computerized process 400 forintelligent distribution of products to keep an inventory at an optimumlevel in FC 200. The process or a portion thereof may be performed byIPS 324. In some embodiments, IPS 324 may repeat steps 401-404 atpredetermined intervals such as once a day. Still further, IPS 324 mayperform process 400 for all, or substantially all, products that havebeen stocked or sold before. Each product may be associated with aunique product identifier such as a stock keeping unit (SKU). IPS 324may include one or more processors and a memory storing instructionsthat, when executed by the one or more processors, cause the system toperform the steps shown in FIG. 4.

In step 401, IPS 324 may receive an order quantity for a product fromTIP 323. As described above with respect to FIG. 3, TIP 323 mayrecommend order quantities based on complex data collected from datascience module 321 and forecast generator 322.

In step 402, IPS 324 may prioritize the order quantity based onreal-world constraints at a national level, such as a capacity oflocation (e.g., FC 200) to handle the product, a popularity of theproduct, etc. The real-world constraints may include a capacity oflocation to handle the product and a popularity of the product, whereinthe popularity of the product is determined by demand forecast data(order quantity) associated with the product or outbound shipments ofthe product. In some embodiment, IPS 324 may calculate the popularity(or “velocity”) of the product on an individual basis, using 80% of thedemand forecast data and 20% of the historical outbound shipments of theproduct (other embodiments and values are possible as well). Thereal-world constraints may further include physical constraints offulfillment centers. For example, a first fulfillment center may becapable of handling a tote (for items to be stored at the firstfulfilment center) having dimensions of 450 mm×385 mm×350 mm. If an itemis bigger than the tote size, the item cannot be fulfilled in the firstfulfillment center because the tote carries the item to transfer withinthe first fulfillment center by a conveyer. IPS 324 may manage suchconstraints associated with the physical constraints to ensure that anitem is not assigned to a fulfillment center which not equipped tohandle the item of such size.

The prioritization may be processed in two ways. The first way comprisesutilizing a set of rules which defines an assignment of predefinedquantity of a product to predetermined locations with capabilities tohandle large volumes of the product. For example, a set of rules maydefine assigning first 5,000 SKUs of the most popular product in fiveFCs, wherein the FCs have capabilities to handle large volumes ofproducts. The assigned SKUs may be further assigned to regionsassociated with the FCs. The second way comprises utilizing a logisticalregression model to prioritize the order quantity using real-worldconstraint inputs. IPS 324 may process different algorithms andconfigurations on the regression model and select the best result. Thealgorithms may comprise a “redundant SKU first” algorithm, a “singlymapped SKU first” algorithm, and a “randomization” algorithm. Theredundant SKU first algorithm may process items (SKU) by theirpopularities from fast to slow, such that a first item associated withfast popularity is assumed to sell quicker than a second associated withslow popularity. The singly mapped SKU algorithm may process an itemwhich is only mapped to a single fulfillment center. The randomizationalgorithm may randomize the order in which SKUs are processed.

In step 403, IPS 324 may assign the prioritized order quantity to one ormore FCs. In some embodiments, IPS 324 may initially assign theprioritized order quantity to locations based on the set of rules. Inanother embodiments, IPS 324 may assign the order quantities to each FCbased on the current product inventory level of each product at each FC;a level of demand for a particular product from each FC; and the like.

Once IPS 324 assigned all prioritized order quantities and determined anestimated delivery date for each product, one or more of the FCs mayhave ended up with a total quantity for a particular date that exceedsthe FC's intake processing capacity for the particular date. In thiscase, IPS 324 may determine an amount of the quantities over the intakeprocessing capacity and transfer corresponding quantities to one or moreother FCs that are below their respective intake processing capacitiesfor the particular date. In this case, IPS 324 may split the exceededamount among the one or more other FCs in any suitable way as long as anintake processing capacity of a receiving FC is not exceeded as aresult. For example, IPS 324 may split the exceeded capacity into equalportions among the other FCs; based on ratio of available capacity ateach FC so that the FCs will end up with the same ratio of availablecapacity (e.g., all FC will have quantities that reach 90% of theirrespective intake processing capacities); or the like. In someembodiments, IPS 324 may transfer a greater portion of the exceededcapacity to FCs nearest to the FC with exceeded capacity or adjust theportions in a way that minimizes any additional shipping cost that mayarise.

In step 404, IPS 324 may communicate to PO generator 326 to generate POsbased on the assigned order quantities for each FC. In one aspect, theremay be more than one PO generator 326, each of which are associated witha particular FC. In this case, the particular PO generator 326 assignedto each FC may generate the POs to the appropriate supplier for theorder quantities distributed to its own FC. In another aspect, POgenerator 326 may be part of a centralized system that generates all POsfor all FCs by changing delivery addresses of the POs based on where aparticular quantity of products is distributed at step 406 above. Acombination of the two embodiments is also possible, where there may bemore than one PO generator 326, each of which are associated with one ormore FCs and are in charge of generating POs for all FCs it isassociated with.

While the present disclosure has been shown and described with referenceto particular embodiments thereof, it will be understood that thepresent disclosure can be practiced, without modification, in otherenvironments. The foregoing description has been presented for purposesof illustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. Various programs orprogram modules can be created using any of the techniques known to oneskilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented first system forintelligent distribution of products, the first system comprising: amemory storing instructions; and at least one processor configured toexecute the instructions for: receiving an order quantity for a productfrom a second system determining optimal order quantities for productsbased on demand forecast data, the demand forecast data being generatedby a model trained to predict a level of future demand based on at leastone of: sales statistics, a number of webpage views, a season, a day ofthe week, or a holiday; prioritizing the order quantity based on actualconstraints; assigning the prioritized order quantity to one or morelocations based on current product inventory levels; determining anexceeded amount of quantities over an intake capacity of a firstlocation; and transferring the exceeded amount of quantities to one ormore remaining locations in equal amounts across the remaininglocations.
 2. The computer-implemented system of claim 1, whereinprioritizing the order quantity comprises utilizing a set of rules. 3.The computer-implemented system of claim 2, wherein the set of rulescomprises an assignment of a predefined quantity of the product topredetermined locations having the capacity to handle volumes more thana predefined quantity.
 4. The computer-implemented system of claim 1,wherein prioritizing the order quantity comprises utilizing a logisticalregression model to prioritize the order quantity using actualconstraints.
 5. The computer-implemented system of claim 1, wherein theconstraints comprise a capacity of location to receive the product and apopularity of the product, wherein the popularity of the product isdetermined by the demand forecast data, the demand forecast data beingassociated with the product or outbound shipments of the product.
 6. Thecomputer-implemented system of claim 1, wherein the locations comprisepredetermined fulfillment centers with capabilities to handle largevolumes of the product.
 7. The computer-implemented system of claim 1,wherein the assigned order quantity is further assigned to differentregions associated with the predetermined locations.
 8. Thecomputer-implemented system of claim 1, wherein the demand forecast datais generated by the trained model based on a number of webpage views ofthe product.
 9. The computer-implemented system of claim 1, wherein thedemand forecast data is generated by the trained model based on acurrent number of orders of the product, the current number of orders ofthe product being received at the computer-implemented system from aplurality of user devices using a network interface.
 10. Thecomputer-implemented system of claim 1, wherein the instructions furthercomprise generating purchase orders to a supplier associated with theproduct based on the assigned order quantity.
 11. A computer-implementedmethod for intelligent distribution of products, the system comprising:receiving an order quantity for a product from a system determiningoptimal order quantities for products based on demand forecast data, thedemand forecast data being generated by a model trained to predict alevel of future demand based on at least one of: sales statistics, anumber of webpage views, a season, a day of the week, or a holiday;prioritizing the order quantity based on actual constraints; andassigning the prioritized order quantity to one or more locations basedon current product inventory levels; determining an exceeded amount ofquantities over an intake capacity of a first location; and transferringthe exceeded amount of quantities to one or more remaining locations inequal amounts across the remaining locations.
 12. The method of claim11, wherein prioritizing the order quantity comprises utilizing a set ofrules.
 13. The method of claim 12, wherein the set of rules comprises anassignment of predefined quantity of the product to predeterminedlocations with capabilities to handle volumes more than a predefinedamount.
 14. The method of claim 11, wherein prioritizing the orderquantity comprises utilizing a logistical regression model to prioritizethe order quantity using actual constraints.
 15. The method of claim 11,wherein the actual constraints comprise a capacity of location toreceive the product and a popularity of the product, wherein thepopularity of the product is determined by the demand forecast data, thedemand forecast data being associated with the product or outboundshipments of the product.
 16. The method of claim 11, wherein thelocations comprise predetermined fulfillment centers with capabilitiesto handle large volumes of the product.
 17. The method of claim 11,wherein the assigned order quantity is further assigned to differentregions associated with the predetermined locations.
 18. The method ofclaim 11, wherein the demand forecast data is generated by the trainedmodel based on a number of webpage views of the product.
 19. The methodof claim 11, wherein the demand forecast data is generated by thetrained model based on a current number of orders of the product, thecurrent number of orders of the product being received at thecomputer-implemented system from a plurality of user devices using anetwork interface.
 20. A computer-implemented system for intelligentdistribution of products, the system comprising: a memory storinginstructions; and at least one processor configured to execute theinstructions for: receiving one or more demand forecast quantities ofone or more products, the products corresponding to one or more productidentifiers, the demand forecast quantities comprising a demand forecastquantity for each product for each unit of time, and the demand forecastquantities being generated by a model trained to predict a level offuture demand based on at least one of: sales statistics, a number ofwebpage views, a season, a day of the week, or a holiday; receivingsupplier statistics data for one or more suppliers, the suppliers beingassociated with a portion of the products; receiving current productinventory levels and currently ordered quantities of the products;determining order quantities for the products based at least on thedemand forecast quantities, the supplier statistics data, and thecurrent product inventory levels; prioritizing the order quantitiesbased on actual constraints, the actual constraints comprising currentproduct inventory levels; assigning the prioritized order quantities toone or more locations; determining an exceeded amount of quantities overan intake capacity of a first location; transferring the exceeded amountof quantities to one or more remaining locations in equal amounts acrossthe remaining locations; and generating purchase orders to the suppliersfor the products based on the assigned order quantities.